Results 21 to 30 of about 691,007 (262)
Learning Latent Jet Structure [PDF]
Symmetry, 2021 We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation.
Barry M. Dillon +3 more
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Synthesis of a novel photoactivatable glucosylceramide cross-linker
Journal of Lipid Research, 2016 The biosynthesis of glucosylceramide (GlcCer) is a key rate-limiting step in complex glycosphingolipid (GSL) biosynthesis. To further define interacting partners of GlcCer, we have made a cleavable, biotinylated, photoreactive GlcCer analog in which the ...
Monique Budani +3 more
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Exact Learning Augmented Naive Bayes Classifier
Entropy, 2021 Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal ...
Shouta Sugahara, Maomi Ueno
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Learning with structured sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009 This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Junzhou Huang +2 more
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Applied Sciences, 2019 This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been
Kang-moon Park +2 more
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Learning Bayesian Networks That Enable Full Propagation of Evidence
IEEE Access, 2020 This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent.
Anthony C. Constantinou
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Remote Sensing, 2023 The reconstruction of 3D shapes from a single view has been a longstanding challenge. Previous methods have primarily focused on learning either geometric features that depict overall shape contours but are insufficient for occluded regions, local ...
Guoqing Gao +5 more
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Structured Priors for Structure Learning
CoRR, 2012 Traditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in.
Vikash Mansinghka 0001 +3 more
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Multiple conformations facilitate PilT function in the type IV pilus
Nature Communications, 2019 Bacterial type IV pilus-like systems catalyse the formation of pilin fibres but it is unknown how they are powered. Here, the authors present crystal and cryo-EM structures of the hexameric motor ATPases PilB and PilT from Type IVa Pilus that reveal ...
Matthew McCallum +6 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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